The difference in size between different kinds of objects will lead to the difference in summed volume region (SVR) of corresponding laser point cloud. In the first frame, object recognition is accomplished based on SVR selection and global feature matching to automatically select the interested object. The performance and execution time of four global feature descriptors are compared. After obtaining the position of the interested object in the first frame, an object tracking method based on global feature matching processing of laser point cloud is put forward for subsequent frames. The experimental results show that adding SVR selection is helpful to improve the accuracy of recognition and tracking and the overall running speed of the algorithm.